Staff Data Engineer, Data Operations
Boston, MA
Wayfair Inc.
Shop Wayfair for A Zillion Things Home across all styles and budgets. 5,000 brands of furniture, lighting, cookware, and more. Free Shipping on most items.Wayfair is in the process of going through a significant digital transformation. With the goal of digitizing and gaining visibility into the entirety of Wayfair’s vast supply chain, Wayfair’s operational supply-chain systems are producing events and data at a massive scale. The Data Operations team is responsible for capturing, transforming, storing, and cataloging these events/data into Wayfair’s supply-chain data lake. Data procured by our team is used by analytics and data science teams to enable business operators to make data-driven decisions that take Wayfair’s supply-chain to the next level.
What you'll do:
- Design, develop, and maintain data movement/transformation platforms/tools that process data at Wayfair scale
- Utilize current and leading edge technologies in software engineering, big data, streaming, and cloud infrastructure
- Lead technical initiatives, provide technical direction/recommendations/best practices, and build reliable and scalable technical solutions
- Plan and lead small to large projects by breaking down technical solutions into actionable tasks
- Produce technical documentation and diagrams
- Be a technical mentor to junior and mid-level engineers
- Stay current on streaming and big data technology trends
Who you are:
- A senior software engineer with roughly 10+ years of relevant experience
- Strong skills in software engineering, with experience in the following:
- Python and JVM languages (Java, Kotlin)
- Container technologies, such as Docker and Kubernetes
- Cloud technologies (Google Cloud Platform preferred)
- Big Data technologies (Hadoop/HDFS/Hive/Presto, Big Query)
- Data pipeline tools (Spark, Flink, Beam, Dataflow)
- Software build tooling and CI/CD pipelines preferred
- Experience with designing data models for traditional relational databases, preferably with experience in data warehouses, data lakes, etc.
- Experience with producing/consuming streaming data and streaming infrastructure (Kafka, Pub/Sub)
- Experience leading small/medium teams utilizing agile methodologies
- A strong collaborator that is capable of working with engineers and stakeholders within and across teams
- Experience with domain-driven design, event modeling, and event sourcing is preferred
About Wayfair Inc.
Wayfair is one of the world’s largest online destinations for the home. Whether you work in our global headquarters in Boston or Berlin, or in our warehouses or offices throughout the world, we’re reinventing the way people shop for their homes. Through our commitment to industry-leading technology and creative problem-solving, we are confident that Wayfair will be home to the most rewarding work of your career. If you’re looking for rapid growth, constant learning, and dynamic challenges, then you’ll find that amazing career opportunities are knocking.
No matter who you are, Wayfair is a place you can call home. We’re a community of innovators, risk-takers, and trailblazers who celebrate our differences, and know that our unique perspectives make us stronger, smarter, and well-positioned for success. We value and rely on the collective voices of our employees, customers, community, and suppliers to help guide us as we build a better Wayfair – and world – for all. Every voice, every perspective matters. That’s why we’re proud to be an equal opportunity employer. We do not discriminate on the basis of race, color, ethnicity, ancestry, religion, sex, national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, gender expression, veteran status, or genetic information.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Agile Big Data BigQuery CI/CD Dataflow Docker Engineering Flink GCP Google Cloud Hadoop HDFS Kafka Kubernetes Pipelines Python RDBMS Spark Streaming
Perks/benefits: Career development Team events
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